Overview

Dataset statistics

Number of variables15
Number of observations404
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 KiB
Average record size in memory120.3 B

Variable types

Numeric14
Categorical1

Alerts

df_index is highly correlated with RAD and 1 other fieldsHigh correlation
CRIM is highly correlated with ZN and 8 other fieldsHigh correlation
ZN is highly correlated with CRIM and 5 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 7 other fieldsHigh correlation
NOX is highly correlated with CRIM and 8 other fieldsHigh correlation
RM is highly correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly correlated with CRIM and 7 other fieldsHigh correlation
DIS is highly correlated with CRIM and 6 other fieldsHigh correlation
RAD is highly correlated with df_index and 3 other fieldsHigh correlation
TAX is highly correlated with df_index and 8 other fieldsHigh correlation
PTRATIO is highly correlated with MEDVHigh correlation
LSTAT is highly correlated with CRIM and 8 other fieldsHigh correlation
MEDV is highly correlated with CRIM and 7 other fieldsHigh correlation
df_index is highly correlated with RAD and 1 other fieldsHigh correlation
CRIM is highly correlated with RAD and 1 other fieldsHigh correlation
ZN is highly correlated with INDUS and 3 other fieldsHigh correlation
INDUS is highly correlated with ZN and 6 other fieldsHigh correlation
NOX is highly correlated with ZN and 6 other fieldsHigh correlation
RM is highly correlated with LSTAT and 1 other fieldsHigh correlation
AGE is highly correlated with ZN and 5 other fieldsHigh correlation
DIS is highly correlated with ZN and 5 other fieldsHigh correlation
RAD is highly correlated with df_index and 4 other fieldsHigh correlation
TAX is highly correlated with df_index and 7 other fieldsHigh correlation
PTRATIO is highly correlated with MEDVHigh correlation
LSTAT is highly correlated with INDUS and 6 other fieldsHigh correlation
MEDV is highly correlated with RM and 2 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 4 other fieldsHigh correlation
ZN is highly correlated with INDUS and 1 other fieldsHigh correlation
INDUS is highly correlated with CRIM and 3 other fieldsHigh correlation
NOX is highly correlated with CRIM and 4 other fieldsHigh correlation
AGE is highly correlated with NOX and 2 other fieldsHigh correlation
DIS is highly correlated with CRIM and 3 other fieldsHigh correlation
RAD is highly correlated with CRIM and 1 other fieldsHigh correlation
TAX is highly correlated with CRIM and 1 other fieldsHigh correlation
LSTAT is highly correlated with AGE and 1 other fieldsHigh correlation
MEDV is highly correlated with LSTATHigh correlation
df_index is highly correlated with ZN and 10 other fieldsHigh correlation
CRIM is highly correlated with INDUS and 3 other fieldsHigh correlation
ZN is highly correlated with df_index and 8 other fieldsHigh correlation
INDUS is highly correlated with df_index and 9 other fieldsHigh correlation
NOX is highly correlated with df_index and 10 other fieldsHigh correlation
RM is highly correlated with PTRATIO and 3 other fieldsHigh correlation
AGE is highly correlated with df_index and 8 other fieldsHigh correlation
DIS is highly correlated with df_index and 9 other fieldsHigh correlation
RAD is highly correlated with df_index and 9 other fieldsHigh correlation
TAX is highly correlated with df_index and 6 other fieldsHigh correlation
PTRATIO is highly correlated with df_index and 10 other fieldsHigh correlation
B is highly correlated with df_index and 3 other fieldsHigh correlation
LSTAT is highly correlated with df_index and 9 other fieldsHigh correlation
MEDV is highly correlated with df_index and 9 other fieldsHigh correlation
df_index has unique values Unique
ZN has 293 (72.5%) zeros Zeros

Reproduction

Analysis started2021-10-30 05:26:53.310058
Analysis finished2021-10-30 05:28:01.850588
Duration1 minute and 8.54 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct404
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.8143564
Minimum0
Maximum505
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:02.212166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.15
Q1121.75
median245
Q3373.25
95-th percentile473.7
Maximum505
Range505
Interquartile range (IQR)251.5

Descriptive statistics

Standard deviation145.3223257
Coefficient of variation (CV)0.5887920291
Kurtosis-1.194203186
Mean246.8143564
Median Absolute Deviation (MAD)126.5
Skewness0.05370653547
Sum99713
Variance21118.57835
MonotonicityNot monotonic
2021-10-30T05:28:02.601093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.2%
3221
 
0.2%
3381
 
0.2%
3371
 
0.2%
3361
 
0.2%
3351
 
0.2%
3331
 
0.2%
3321
 
0.2%
3311
 
0.2%
3281
 
0.2%
Other values (394)394
97.5%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
31
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
121
0.2%
131
0.2%
ValueCountFrequency (%)
5051
0.2%
5041
0.2%
5021
0.2%
5001
0.2%
4991
0.2%
4981
0.2%
4961
0.2%
4951
0.2%
4931
0.2%
4921
0.2%

CRIM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct402
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.69745495
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:03.257996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.027358
Q10.0825975
median0.234405
Q33.5949275
95-th percentile16.67119
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.51233

Descriptive statistics

Standard deviation9.146742709
Coefficient of variation (CV)2.473794226
Kurtosis35.63375544
Mean3.69745495
Median Absolute Deviation (MAD)0.199565
Skewness5.227843948
Sum1493.7718
Variance83.66290219
MonotonicityNot monotonic
2021-10-30T05:28:03.606706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.5%
14.33372
 
0.5%
0.578341
 
0.2%
0.035481
 
0.2%
0.955771
 
0.2%
0.041131
 
0.2%
0.035371
 
0.2%
1.387991
 
0.2%
0.520141
 
0.2%
0.976171
 
0.2%
Other values (392)392
97.0%
ValueCountFrequency (%)
0.006321
0.2%
0.009061
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.5%
0.015381
0.2%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
28.65581
0.2%
25.94061
0.2%
25.04611
0.2%

ZN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.52722772
Minimum0
Maximum100
Zeros293
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:03.965539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.28828396
Coefficient of variation (CV)2.020284888
Kurtosis4.15345176
Mean11.52722772
Median Absolute Deviation (MAD)0
Skewness2.234782342
Sum4657
Variance542.34417
MonotonicityNot monotonic
2021-10-30T05:28:04.299132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0293
72.5%
2019
 
4.7%
8011
 
2.7%
259
 
2.2%
229
 
2.2%
12.57
 
1.7%
406
 
1.5%
305
 
1.2%
905
 
1.2%
455
 
1.2%
Other values (15)35
 
8.7%
ValueCountFrequency (%)
0293
72.5%
12.57
 
1.7%
17.51
 
0.2%
181
 
0.2%
2019
 
4.7%
214
 
1.0%
229
 
2.2%
259
 
2.2%
282
 
0.5%
305
 
1.2%
ValueCountFrequency (%)
1001
 
0.2%
953
 
0.7%
905
1.2%
852
 
0.5%
82.51
 
0.2%
8011
2.7%
753
 
0.7%
702
 
0.5%
603
 
0.7%
52.53
 
0.7%

INDUS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.0775
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:04.630293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.125
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.848411921
Coefficient of variation (CV)0.6182272102
Kurtosis-1.232672408
Mean11.0775
Median Absolute Deviation (MAD)5.715
Skewness0.3126345632
Sum4475.31
Variance46.90074584
MonotonicityNot monotonic
2021-10-30T05:28:04.972695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1104
25.7%
19.5823
 
5.7%
8.1418
 
4.5%
6.213
 
3.2%
21.8912
 
3.0%
3.9710
 
2.5%
10.5910
 
2.5%
5.869
 
2.2%
6.918
 
2.0%
8.568
 
2.0%
Other values (62)189
46.8%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.251
 
0.2%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.5%
1.524
1.0%
1.691
 
0.2%
ValueCountFrequency (%)
27.743
 
0.7%
25.657
 
1.7%
21.8912
 
3.0%
19.5823
 
5.7%
18.1104
25.7%
15.042
 
0.5%
13.924
 
1.0%
13.893
 
0.7%
12.836
 
1.5%
11.933
 
0.7%

CHAS
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0.0
372 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0372
92.1%
1.032
 
7.9%

Length

2021-10-30T05:28:05.333608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-30T05:28:05.556198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0372
92.1%
1.032
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NOX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct76
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5530262376
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:05.765550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40915
Q10.448
median0.535
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.176

Descriptive statistics

Standard deviation0.1168946844
Coefficient of variation (CV)0.2113727641
Kurtosis-0.07277225706
Mean0.5530262376
Median Absolute Deviation (MAD)0.089
Skewness0.7496713633
Sum223.4226
Variance0.01366436725
MonotonicityNot monotonic
2021-10-30T05:28:06.147725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53819
 
4.7%
0.71317
 
4.2%
0.43715
 
3.7%
0.87113
 
3.2%
0.48913
 
3.2%
0.62412
 
3.0%
0.69311
 
2.7%
0.60510
 
2.5%
0.749
 
2.2%
0.79
 
2.2%
Other values (66)276
68.3%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3922
0.5%
0.3941
 
0.2%
0.43
0.7%
0.4013
0.7%
0.4033
0.7%
0.4043
0.7%
0.4053
0.7%
0.4092
0.5%
0.413
0.7%
ValueCountFrequency (%)
0.87113
3.2%
0.777
1.7%
0.749
2.2%
0.7183
 
0.7%
0.71317
4.2%
0.79
2.2%
0.69311
2.7%
0.6796
 
1.5%
0.6716
 
1.5%
0.6683
 
0.7%

RM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.268792079
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:06.505240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.31
Q15.87675
median6.179
Q36.6265
95-th percentile7.4676
Maximum8.78
Range5.219
Interquartile range (IQR)0.74975

Descriptive statistics

Standard deviation0.6892286632
Coefficient of variation (CV)0.1099460079
Kurtosis1.676887523
Mean6.268792079
Median Absolute Deviation (MAD)0.3405
Skewness0.2821905315
Sum2532.592
Variance0.4750361502
MonotonicityNot monotonic
2021-10-30T05:28:06.901963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1673
 
0.7%
6.2293
 
0.7%
5.7133
 
0.7%
6.4052
 
0.5%
6.3152
 
0.5%
6.9512
 
0.5%
6.1272
 
0.5%
6.7272
 
0.5%
6.1932
 
0.5%
6.3762
 
0.5%
Other values (356)381
94.3%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1381
0.2%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
4.971
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.3981
0.2%
8.3751
0.2%
8.2971
0.2%
8.2591
0.2%
8.0691
0.2%
8.041
0.2%
7.9291
0.2%
7.9231
0.2%
7.8751
0.2%

AGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct302
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.93564356
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:07.285028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.5
Q143.25
median76.8
Q393.825
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)50.575

Descriptive statistics

Standard deviation28.56318609
Coefficient of variation (CV)0.4204447709
Kurtosis-1.009870706
Mean67.93564356
Median Absolute Deviation (MAD)20.2
Skewness-0.5791546473
Sum27446
Variance815.8555998
MonotonicityNot monotonic
2021-10-30T05:28:07.672750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10035
 
8.7%
87.94
 
1.0%
964
 
1.0%
95.63
 
0.7%
97.33
 
0.7%
97.93
 
0.7%
95.43
 
0.7%
973
 
0.7%
36.63
 
0.7%
94.13
 
0.7%
Other values (292)340
84.2%
ValueCountFrequency (%)
2.91
0.2%
61
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.5%
6.81
0.2%
7.82
0.5%
8.41
0.2%
8.91
0.2%
9.91
0.2%
ValueCountFrequency (%)
10035
8.7%
99.31
 
0.2%
99.11
 
0.2%
98.91
 
0.2%
98.82
 
0.5%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.5%
98.31
 
0.2%
98.22
 
0.5%

DIS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct339
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.826110644
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:08.071720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.45632
Q12.10535
median3.2986
Q35.141475
95-th percentile7.935835
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.036125

Descriptive statistics

Standard deviation2.120998991
Coefficient of variation (CV)0.5543485771
Kurtosis0.527037588
Mean3.826110644
Median Absolute Deviation (MAD)1.33105
Skewness1.018401402
Sum1545.7487
Variance4.498636721
MonotonicityNot monotonic
2021-10-30T05:28:08.645800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.2%
5.72094
 
1.0%
5.28734
 
1.0%
6.81474
 
1.0%
5.40074
 
1.0%
7.31723
 
0.7%
6.47983
 
0.7%
7.3093
 
0.7%
4.81223
 
0.7%
3.94543
 
0.7%
Other values (329)368
91.1%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
1.34591
0.2%
1.35671
0.2%
1.3581
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71031
0.2%
10.58572
0.5%
9.22291
0.2%
9.22032
0.5%
9.18761
0.2%
9.08921
0.2%
8.90672
0.5%
8.79211
0.2%
8.69661
0.2%

RAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.47029703
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:08.965896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.680236753
Coefficient of variation (CV)0.9165749211
Kurtosis-0.8248875946
Mean9.47029703
Median Absolute Deviation (MAD)2
Skewness1.02475827
Sum3826
Variance75.34651009
MonotonicityNot monotonic
2021-10-30T05:28:09.258417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24104
25.7%
592
22.8%
485
21.0%
332
 
7.9%
222
 
5.4%
621
 
5.2%
818
 
4.5%
715
 
3.7%
115
 
3.7%
ValueCountFrequency (%)
115
 
3.7%
222
 
5.4%
332
 
7.9%
485
21.0%
592
22.8%
621
 
5.2%
715
 
3.7%
818
 
4.5%
24104
25.7%
ValueCountFrequency (%)
24104
25.7%
818
 
4.5%
715
 
3.7%
621
 
5.2%
592
22.8%
485
21.0%
332
 
7.9%
222
 
5.4%
115
 
3.7%

TAX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.2574257
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:09.588353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile216.9
Q1277
median329
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)389

Descriptive statistics

Standard deviation169.0304804
Coefficient of variation (CV)0.4191627223
Kurtosis-1.100215829
Mean403.2574257
Median Absolute Deviation (MAD)74
Skewness0.7026853169
Sum162916
Variance28571.30329
MonotonicityNot monotonic
2021-10-30T05:28:09.980067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666104
25.7%
30731
 
7.7%
40323
 
5.7%
43712
 
3.0%
39811
 
2.7%
27710
 
2.5%
30410
 
2.5%
26410
 
2.5%
3309
 
2.2%
3848
 
2.0%
Other values (53)176
43.6%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.7%
1937
1.7%
1981
 
0.2%
2165
1.2%
2224
1.0%
2235
1.2%
2248
2.0%
2261
 
0.2%
2338
2.0%
ValueCountFrequency (%)
7113
 
0.7%
666104
25.7%
4691
 
0.2%
43712
 
3.0%
4327
 
1.7%
4301
 
0.2%
4111
 
0.2%
40323
 
5.7%
4022
 
0.5%
39811
 
2.7%

PTRATIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.43861386
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:10.363805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.225
median19
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.975

Descriptive statistics

Standard deviation2.169468737
Coefficient of variation (CV)0.1176589929
Kurtosis-0.2321044426
Mean18.43861386
Median Absolute Deviation (MAD)1.2
Skewness-0.8066332559
Sum7449.2
Variance4.7065946
MonotonicityNot monotonic
2021-10-30T05:28:10.784826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2111
27.5%
14.725
 
6.2%
2121
 
5.2%
17.819
 
4.7%
18.616
 
4.0%
19.116
 
4.0%
19.215
 
3.7%
16.615
 
3.7%
17.413
 
3.2%
21.212
 
3.0%
Other values (36)141
34.9%
ValueCountFrequency (%)
12.63
 
0.7%
1310
 
2.5%
13.61
 
0.2%
14.41
 
0.2%
14.725
6.2%
14.82
 
0.5%
14.94
 
1.0%
15.11
 
0.2%
15.210
 
2.5%
15.32
 
0.5%
ValueCountFrequency (%)
222
 
0.5%
21.212
 
3.0%
21.11
 
0.2%
2121
 
5.2%
20.98
 
2.0%
20.2111
27.5%
20.13
 
0.7%
19.76
 
1.5%
19.65
 
1.2%
19.215
 
3.7%

B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct287
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.1536881
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:11.162484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile88.049
Q1376.0925
median391.575
Q3396.1575
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.065

Descriptive statistics

Standard deviation91.5416474
Coefficient of variation (CV)0.2563088397
Kurtosis7.383862826
Mean357.1536881
Median Absolute Deviation (MAD)5.325
Skewness-2.921604753
Sum144290.09
Variance8379.873208
MonotonicityNot monotonic
2021-10-30T05:28:11.541804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.996
 
23.8%
395.243
 
0.7%
393.743
 
0.7%
341.62
 
0.5%
395.622
 
0.5%
377.072
 
0.5%
376.142
 
0.5%
393.232
 
0.5%
395.582
 
0.5%
392.782
 
0.5%
Other values (277)288
71.3%
ValueCountFrequency (%)
0.321
0.2%
2.521
0.2%
2.61
0.2%
3.51
0.2%
3.651
0.2%
6.681
0.2%
9.321
0.2%
10.481
0.2%
16.451
0.2%
21.571
0.2%
ValueCountFrequency (%)
396.996
23.8%
396.331
 
0.2%
396.281
 
0.2%
396.241
 
0.2%
396.231
 
0.2%
396.211
 
0.2%
396.141
 
0.2%
396.062
 
0.5%
395.991
 
0.2%
395.931
 
0.2%

LSTAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7785396
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:11.934023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7045
Q17.0925
median11.465
Q317.1025
95-th percentile26.8125
Maximum37.97
Range36.24
Interquartile range (IQR)10.01

Descriptive statistics

Standard deviation7.216402642
Coefficient of variation (CV)0.5647282761
Kurtosis0.5613141428
Mean12.7785396
Median Absolute Deviation (MAD)4.82
Skewness0.9211819112
Sum5162.53
Variance52.07646709
MonotonicityNot monotonic
2021-10-30T05:28:12.288574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.053
 
0.7%
7.793
 
0.7%
6.363
 
0.7%
18.133
 
0.7%
15.172
 
0.5%
23.982
 
0.5%
13.152
 
0.5%
10.112
 
0.5%
3.762
 
0.5%
14.812
 
0.5%
Other values (356)380
94.1%
ValueCountFrequency (%)
1.731
0.2%
1.981
0.2%
2.871
0.2%
2.941
0.2%
2.971
0.2%
2.981
0.2%
3.011
0.2%
3.112
0.5%
3.131
0.2%
3.162
0.5%
ValueCountFrequency (%)
37.971
0.2%
36.981
0.2%
34.771
0.2%
34.411
0.2%
34.371
0.2%
34.021
0.2%
31.991
0.2%
30.812
0.5%
30.631
0.2%
30.621
0.2%

MEDV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct210
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.52227723
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2021-10-30T05:28:12.684438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.23
Q117.175
median21.05
Q325.225
95-th percentile42.15
Maximum50
Range45
Interquartile range (IQR)8.05

Descriptive statistics

Standard deviation8.998990777
Coefficient of variation (CV)0.3995595421
Kurtosis1.469384182
Mean22.52227723
Median Absolute Deviation (MAD)3.95
Skewness1.056975148
Sum9099
Variance80.981835
MonotonicityNot monotonic
2021-10-30T05:28:13.035049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5011
 
2.7%
23.17
 
1.7%
256
 
1.5%
20.66
 
1.5%
21.25
 
1.2%
17.85
 
1.2%
21.75
 
1.2%
19.65
 
1.2%
225
 
1.2%
21.45
 
1.2%
Other values (200)344
85.1%
ValueCountFrequency (%)
52
0.5%
5.61
 
0.2%
72
0.5%
7.23
0.7%
7.41
 
0.2%
7.51
 
0.2%
8.31
 
0.2%
8.41
 
0.2%
8.51
 
0.2%
8.71
 
0.2%
ValueCountFrequency (%)
5011
2.7%
48.81
 
0.2%
48.51
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
441
 
0.2%
43.81
 
0.2%
43.51
 
0.2%
42.81
 
0.2%

Interactions

2021-10-30T05:27:56.090677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:26:58.783965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:03.286033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:07.621020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:12.061937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:16.514437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:21.083844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:25.648442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:30.174619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:34.320501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:38.541238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:42.784370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:47.166764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:51.613740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:56.364545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:26:59.147351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:03.576547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:07.927276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:12.339575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:16.826897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:21.376822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:25.952291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:30.490792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:34.602347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:38.833911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:43.101169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:47.487105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:51.910884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:56.636645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:26:59.440544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:03.880998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:08.211523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:12.644852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:17.080505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:21.647892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:26.287646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:30.773882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:34.871207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:39.117814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:43.368369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:47.800033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:52.201242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:56.942229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:26:59.942146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:04.182101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:08.484195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:12.936486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:17.371663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:21.978889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:26.574228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:31.068707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:35.161899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:39.401221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:43.673291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:48.109024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:52.551740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:57.203764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:00.236309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:04.492219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:08.778450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:13.254869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:17.678162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:22.294782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:26.941924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:31.349605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:35.439987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:39.684480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:43.976451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:48.418755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:52.855738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:57.483899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:00.553982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:04.779538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:09.300108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:13.540431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:17.996112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:22.606780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:27.234611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:31.632509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:35.712444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:39.949949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:44.290393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:48.707205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:53.186846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:57.748872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:00.843236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:05.075491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:09.628211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:13.837367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:18.515432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:23.031189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:27.554625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:31.908370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:36.027932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:40.259586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:44.576537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:49.025345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:53.490932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:58.035609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:01.152921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:05.363557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:09.941558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:14.165344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:18.801590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:23.374850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:28.060711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:32.194439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:36.298818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:40.608142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:44.887646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:49.332625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:53.793600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:58.350164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:01.449614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:05.659786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:10.250505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:14.525504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:19.155013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:23.733998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:28.322671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:32.494367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:36.813236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:40.903057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:45.181534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:49.644752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:54.073580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:58.646867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:01.743190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:05.944072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:10.561849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:14.969317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:19.456846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:24.021776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:28.623681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:32.789710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:37.072128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:41.178307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:45.677598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:49.979331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:54.607682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:58.944520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:02.074994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:06.222008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:10.840804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:15.245868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:19.816400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:24.393499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:28.939556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:33.060391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:37.352675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:41.532301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:45.994172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:50.283354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:54.879378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:59.216528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:02.349756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:06.495995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:11.120558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:15.575458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:20.095247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:24.679155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:29.226839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:33.401426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:37.644847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:41.821646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:46.265390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:50.641379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:55.158920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:59.513404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:02.670537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-30T05:27:11.438038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:15.895731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:20.490912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:25.048945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:29.556322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:33.744198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:37.966771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:42.125888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:46.562814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:50.975111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:55.485905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:59.829848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:02.995180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:07.342844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:11.762944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:16.227308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:20.778240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:25.365012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:29.893364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:34.052657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:38.276031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:42.470886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:46.902543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:51.303345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-30T05:27:55.807733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-30T05:28:13.423941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-30T05:28:14.232924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-30T05:28:14.733747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-30T05:28:15.270916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-30T05:28:00.554029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-30T05:28:01.512559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
0420.141500.06.910.00.4486.1696.65.72093.0233.017.9383.375.8125.3
1580.1544525.05.130.00.4536.14529.27.81488.0284.019.7390.686.8623.3
238516.811800.018.100.00.7005.27798.11.426124.0666.020.2396.9030.817.2
3780.056460.012.830.00.4376.23253.75.01415.0398.018.7386.4012.3421.2
44248.792120.018.100.00.5845.56570.62.063524.0666.020.23.6517.1611.7
51601.273460.019.581.00.6056.25092.61.79845.0403.014.7338.925.5027.0
61850.060470.02.460.00.4886.15368.83.27973.0193.017.8387.1113.1529.6
71010.114320.08.560.00.5206.78171.32.85615.0384.020.9395.587.6726.5
82680.5405020.03.970.00.5757.47052.62.87205.0264.013.0390.303.1643.5
91730.091780.04.050.00.5106.41684.12.64635.0296.016.6395.509.0423.6

Last rows

df_indexCRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
3944489.329090.018.100.00.71306.18598.72.261624.0666.020.2396.9018.1314.1
3953350.039610.05.190.00.51506.03734.55.98535.0224.020.2396.908.0121.1
3961330.329820.021.890.00.62405.82295.42.46994.0437.021.2388.6915.0318.4
3972030.0351095.02.680.00.41617.85333.25.11804.0224.014.7392.783.8148.5
3983938.644760.018.100.00.69306.19392.61.791224.0666.020.2396.9015.1713.8
3992550.0354880.03.640.00.39205.87619.19.22031.0315.016.4395.189.2520.9
400720.091640.010.810.00.41306.0657.85.28734.0305.019.2390.915.5222.8
4013965.872050.018.100.00.69306.40596.01.676824.0666.020.2396.9019.3712.5
4022350.330450.06.200.00.50706.08661.53.65198.0307.017.4376.7510.8824.0
403370.080140.05.960.00.49905.85041.53.93425.0279.019.2396.908.7721.0